Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	126,096
2	South Dakota	119,578
3	Utah	101,754
4	Rhode Island	101,066
5	Tennessee	99,195
6	Wisconsin	98,114
7	Iowa	97,399
8	Nebraska	94,526
9	Arizona	94,251
10	Arkansas	90,659
11	Oklahoma	90,567
12	Kansas	90,181
13	Indiana	88,899
14	Idaho	87,892
15	Alabama	86,993
16	Wyoming	86,256
17	Nevada	85,786
18	Mississippi	85,723
19	Illinois	85,173
20	Montana	84,163
21	Louisiana	80,039
22	Minnesota	79,497
23	New Mexico	78,668
24	California	77,725
25	Missouri	76,863
26	South Carolina	76,585
27	Georgia	75,772
28	Kentucky	74,836
29	Texas	74,509
30	Florida	73,987
31	Delaware	73,232
32	New Jersey	71,570
33	Ohio	71,524
34	Alaska	70,964
35	Massachusetts	69,136
36	Colorado	65,986
37	North Carolina	65,264
38	New York	65,229
39	Connecticut	65,133
40	West Virginia	61,836
41	Pennsylvania	61,190
42	Michigan	58,697
43	Maryland	54,648
44	Virginia	52,846
45	District of Columbia	48,542
46	New Hampshire	43,177
47	Washington	38,939
48	Puerto Rico	38,196
49	Oregon	31,881
50	Maine	25,488
51	Hawaii	17,291
52	Vermont	16,540

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Virginia	846
2	Rhode Island	825
3	Connecticut	822
4	California	811
5	South Carolina	770
6	Arizona	723
7	New York	682
8	New Hampshire	677
9	Delaware	663
10	Georgia	610
11	Oklahoma	565
12	Texas	524
13	Massachusetts	522
14	Louisiana	518
15	New Jersey	510
16	Kentucky	508
17	West Virginia	501
18	Wyoming	491
19	North Carolina	483
20	Mississippi	476
21	Tennessee	453
22	Florida	448
23	Nevada	434
24	Pennsylvania	423
25	Indiana	415
26	Ohio	414
27	Utah	412
28	Kansas	400
29	Alabama	398
30	Arkansas	377
31	District of Columbia	341
32	Maryland	341
33	New Mexico	327
34	Idaho	319
35	Illinois	317
36	Nebraska	301
37	Wisconsin	285
38	Montana	279
39	Iowa	269
40	Missouri	266
41	Colorado	262
42	Alaska	258
43	Maine	258
44	Washington	246
45	Vermont	216
46	Michigan	197
47	Puerto Rico	193
48	South Dakota	191
49	Minnesota	190
50	Oregon	162
51	North Dakota	156
52	Hawaii	69

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,309
2	New York	2,104
3	Massachusetts	1,994
4	Rhode Island	1,930
5	South Dakota	1,884
6	Connecticut	1,874
7	Mississippi	1,872
8	North Dakota	1,847
9	Louisiana	1,790
10	Illinois	1,590
11	Arizona	1,547
12	Pennsylvania	1,520
13	Michigan	1,474
14	Arkansas	1,453
15	New Mexico	1,419
16	Indiana	1,406
17	Iowa	1,373
18	Alabama	1,249
19	Nevada	1,231
20	Tennessee	1,228
21	District of Columbia	1,219
22	South Carolina	1,215
23	Kansas	1,209
24	Georgia	1,147
25	Texas	1,144
26	Florida	1,137
27	Maryland	1,099
28	Missouri	1,087
29	Minnesota	1,066
30	Delaware	1,048
31	Wisconsin	1,026
32	Montana	1,022
33	West Virginia	1,012
34	Nebraska	983
35	Colorado	950
36	Wyoming	950
37	Idaho	917
38	Ohio	884
39	California	870
40	North Carolina	781
41	Kentucky	780
42	Oklahoma	767
43	New Hampshire	689
44	Virginia	679
45	Puerto Rico	533
46	Washington	527
47	Utah	470
48	Oregon	430
49	Maine	386
50	Alaska	302
51	Vermont	261
52	Hawaii	226

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Louisiana	17
2	Wyoming	16
3	Rhode Island	12
4	South Dakota	12
5	Arkansas	10
6	Mississippi	10
7	New Mexico	10
8	West Virginia	10
9	New York	9
10	South Carolina	9
11	California	8
12	Connecticut	8
13	Georgia	8
14	Indiana	8
15	Massachusetts	8
16	Oklahoma	7
17	Pennsylvania	7
18	District of Columbia	6
19	Florida	6
20	Maryland	6
21	Texas	6
22	Idaho	5
23	Kentucky	5
24	Ohio	5
25	Tennessee	5
26	North Carolina	4
27	Illinois	3
28	Minnesota	3
29	Nevada	3
30	New Jersey	3
31	Virginia	3
32	Wisconsin	3
33	Kansas	2
34	Maine	2
35	Michigan	2
36	New Hampshire	2
37	North Dakota	2
38	Utah	2
39	Washington	2
40	Colorado	1
41	Delaware	1
42	Iowa	1
43	Missouri	1
44	Montana	1
45	Nebraska	1
46	Oregon	1
47	Alabama	0
48	Alaska	0
49	Arizona	0
50	Hawaii	0
51	Puerto Rico	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	287,741	1	99
Dewey	South Dakota	232,519	2	99
Bent	Colorado	227,542	3	99
Lincoln	Arkansas	227,503	4	99
Norton	Kansas	223,279	5	99
Davidson	Tennessee	115,848	251	92
Richland	South Carolina	77,451	1498	52
Orange	California	71,582	1791	42
York	South Carolina	71,030	1816	42
Pierce	Washington	35,878	2883	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Dickey	North Dakota	6,568	3	99
Grant	Nebraska	6,421	4	99
Iron	Wisconsin	6,330	5	99
Davidson	Tennessee	968	1869	40
Richland	South Carolina	919	1940	38
Orange	California	780	2163	31
York	South Carolina	726	2241	28
Pierce	Washington	465	2625	16

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.